
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
pd.set_option("display.max_rows", None, "display.max_columns", None)


from google.colab import drive
drive.mount('/content/gdrive')
file_path = '/content/sample_data/brain_stroke.csv'
strokedata_df = pd.read_csv(file_path)

Mounted at /content/gdrive


strokedata_df.head()


print(strokedata_df.columns)
strokedata_df = strokedata_df.loc[:, ~strokedata_df.columns.isin(["ever_married", "work_type"])]
strokedata_df = strokedata_df.reset_index(drop=True)

#dropping ever married variable and work type variable as they are not needed for what we will be exploring

Index(['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',
       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',
       'smoking_status', 'stroke'],
      dtype='object')


strokedata_df


counts=strokedata_df['stroke'].value_counts()
print(counts)
#248 people in the data set had a stroke (1 indicates stroke, 0 indicates no stroke)

stroke
0    4733
1     248
Name: count, dtype: int64


hyp_counts=strokedata_df['hypertension'].value_counts()
print(hyp_counts)
#479 people in the data set had hypertension (1 indicates hypertension and 0 indicates no hypertension)

hypertension
0    4502
1     479
Name: count, dtype: int64


def replace_empty_with_NAN(entry):
    entry = str(entry)
    if re.search("–", entry) != None:
        return np.nan
    elif re.search('="0"',entry) != None:
        return np.nan
    elif re.search('="0.00"', entry) != None:
        return np.nan
    return entry


for col in strokedata_df.columns:
    strokedata_df[col] = strokedata_df[col].apply(replace_empty_with_NAN)


strokedata_df.dtypes
#needs to be formatted

gender               object
age                  object
hypertension         object
heart_disease        object
Residence_type       object
avg_glucose_level    object
bmi                  object
smoking_status       object
stroke               object
dtype: object


lst_of_col = ["heart_disease", "hypertension", "stroke"]

for col in lst_of_col:
    strokedata_df[col] = strokedata_df[col].astype('bool')


lst_of_col = ["avg_glucose_level", "bmi", "age"]

for col in lst_of_col:
    strokedata_df[col] = strokedata_df[col].astype('float64')
strokedata_df.dtypes

#changed dtypes for proper formatting

gender                object
age                  float64
hypertension            bool
heart_disease           bool
Residence_type        object
avg_glucose_level    float64
bmi                  float64
smoking_status        object
stroke                  bool
dtype: object


strokedata_df


strokedata_df['hypertension_and_stroke'] = strokedata_df['hypertension'] & strokedata_df['stroke']


# # Assuming 'hypertension' and 'stroke' columns already exist in strokedata_df
# strokedata_df_new= strokedata_df.apply(lambda row: 'Yes' if row['hypertension'] == 1 and row['stroke'] == 1 else 'No', axis=1)

# count_hyp_with_stroke = strokedata_df["hypertension_stroke"].value_counts()
# print(strokedata_df_new)


# Create a dictionary to store the counts.
stroke_counts = {}

# Loop through each row in the data.
for index, row in strokedata_df.iterrows():
    # Get the gender and stroke values.
    gender = row["gender"]
    stroke = row["stroke"]

    # If the gender is not already in the dictionary, add it.
    if gender not in stroke_counts:
        stroke_counts[gender] = {}

    # If the stroke is not already in the dictionary for the gender, add it.
    if stroke not in stroke_counts[gender]:
        stroke_counts[gender][stroke] = 0

    # Increment the count for the stroke.
    stroke_counts[gender][stroke] += 1

# Print the counts.
for gender, strokes in stroke_counts.items():
    print(f"{gender}:")
    for stroke, count in strokes.items():
        print(f"\t{stroke}: {count}")

Male:
	True: 2074
Female:
	True: 2907


import matplotlib.pyplot as plt

# Create a dictionary to store the counts.
stroke_counts = {}

# Loop through each row in the data.
for index, row in strokedata_df.iterrows():
    # Get the gender and stroke values.
    gender = row["gender"]
    stroke = row["stroke"]

    # If the gender is not already in the dictionary, add it.
    if gender not in stroke_counts:
        stroke_counts[gender] = {}

    # If the stroke is not already in the dictionary for the gender, add it.
    if stroke not in stroke_counts[gender]:
        stroke_counts[gender][stroke] = 0

    # Increment the count for the stroke.
    stroke_counts[gender][stroke] += 1

# Plot the counts.
bar_width = 0.35
x_offset = 0

for gender, strokes in stroke_counts.items():
    stroke_names = list(strokes.keys())
    counts = list(strokes.values())

    x = [i + x_offset for i in range(len(stroke_names))]
    plt.bar(x, counts, label=gender, width=bar_width)
    x_offset += bar_width

# Add labels and title.
plt.xticks([i + (x_offset - bar_width) / 2 for i in range(len(stroke_names))], stroke_names)
plt.xlabel("Stroke Type")
plt.ylabel("Count")
plt.title("Stroke Counts by Gender")

# Add legend and show the plot.
plt.legend()
plt.tight_layout()
plt.show()

#females seem to have more strokes


strokedata_df.describe()
#used to get averages and counts of float64 data, average age was 43.42, average glucose level was 105.94, and average BMI was 28.50


import seaborn as sns

sns.boxplot(data=strokedata_df.describe().drop(index=['count']))
plt.show()


# Create a new column 'high_bmi_stroke' using a lambda function
strokedata_df['high_bmi_stroke'] = strokedata_df.apply(lambda row: 'Yes' if row['bmi'] >= 30 and row['stroke'] == 1 else 'No', axis=1)

count_high_bmi_stroke = strokedata_df['high_bmi_stroke'].value_counts()

print(count_high_bmi_stroke)
percentage_high_bmi_stroke = count_high_bmi_stroke / len(strokedata_df) * 100

print("Percentage of 'Yes': {:.2f}%".format(percentage_high_bmi_stroke['Yes']))
print("Percentage of 'No': {:.2f}%".format(percentage_high_bmi_stroke['No']))

# Plot the counts of 'high_bmi_stroke'
sns.countplot(data=strokedata_df, x='high_bmi_stroke', hue='high_bmi_stroke', palette=['blue', 'red'])
plt.xlabel('High BMI & Stroke')
plt.ylabel('Count')
plt.title('Counts of High BMI and Stroke')
plt.legend(title='High BMI & Stroke', labels=['Yes', 'No'])
plt.show()
#this shows the number of individuals that had high BMI (30 or higher) that had a stroke. A BMI of 30 or greater is the classification for obesity. There were 1925 indiviuals that were obese and had a stroke which is about 39%.

high_bmi_stroke
No     3056
Yes    1925
Name: count, dtype: int64
Percentage of 'Yes': 38.65%
Percentage of 'No': 61.35%


# Histograms for numerical columns
plt.figure(figsize=(12, 6))
plt.subplot(2, 2, 1)
plt.hist(strokedata_df['age'], bins=20, color='skyblue', edgecolor='black')
plt.title('Distribution of Age')
plt.xlabel('Age')
plt.ylabel('Frequency')

plt.subplot(2, 2, 2)
plt.hist(strokedata_df['avg_glucose_level'], bins=20, color='lightgreen', edgecolor='black')
plt.title('Distribution of Average Glucose Level')
plt.xlabel('Average Glucose Level')
plt.ylabel('Frequency')

plt.subplot(2, 2, 3)
plt.hist(strokedata_df['bmi'], bins=20, color='salmon', edgecolor='black')
plt.title('Distribution of BMI')
plt.xlabel('BMI')
plt.ylabel('Frequency')

# Box plots for numerical columns
plt.subplot(2, 2, 4)
sns.boxplot(data=strokedata_df[['age', 'avg_glucose_level', 'bmi']])
plt.title('Box Plot of Age, Average Glucose Level, and BMI')
plt.show()


# Count plot of 'stroke' by 'gender'
plt.figure(figsize=(8, 6))
sns.countplot(data=strokedata_df, x='stroke', hue='gender', palette='Set2')
plt.title('Count Plot of Stroke by Gender')
plt.xlabel('Stroke')
plt.ylabel('Count')
plt.legend(title='Gender')
plt.show()


# Create the new column 'high_bmi_stroke' for females only using a lambda function
strokedata_df['female_high_bmi_stroke'] = strokedata_df.apply(lambda row: 'Yes' if row['gender'] == 'Female' and row['bmi'] >= 30 and row['stroke'] == 1 else 'No' if row['gender'] == 'Female' else 'Male', axis=1)
count_female_high_bmi_stroke = strokedata_df['female_high_bmi_stroke'].value_counts()

print(count_female_high_bmi_stroke)
percentagefemale_high_bmi_stroke = count_female_high_bmi_stroke / len(strokedata_df) * 100

print("Percentage of 'Yes': {:.2f}%".format(percentagefemale_high_bmi_stroke['Yes']))
print("Percentage of 'No': {:.2f}%".format(percentagefemale_high_bmi_stroke['No']))

female_high_bmi_stroke
Male    2074
No      1815
Yes     1092
Name: count, dtype: int64
Percentage of 'Yes': 21.92%
Percentage of 'No': 36.44%


strokedata_df.head()


strokedata_df['female_high_bmi_stroke'].unique()

array(['Male', 'Yes', 'No'], dtype=object)


df_female = strokedata_df[strokedata_df['gender'] != 'Male'].reset_index(drop=True)


df_female


data = {
    'female_high_bmi_stroke': ['Yes', 'No'],
    'count': [1092, 1815]  # counts determined from cell above
}
df_female = pd.DataFrame(data)

colors = ['blue', 'green']

# Create the bar plot
plt.bar(df_female['female_high_bmi_stroke'], df_female['count'], color=colors)

# Add labels and title
plt.xlabel('High BMI and Stroke in Females')
plt.ylabel('Count')
plt.title('Count of High BMI and Stroke in Females')

# Show plot
plt.show()


# Create the new column 'high_bmi_stroke' for males only using a lambda function
strokedata_df['male_high_bmi_stroke'] = strokedata_df.apply(lambda row: 'Yes' if row['gender'] == 'Male' and row['bmi'] >= 30 and row['stroke'] == 1 else 'No' if row['gender'] == 'Male' else 'Female', axis=1)
count_male_high_bmi_stroke = strokedata_df['male_high_bmi_stroke'].value_counts()

print(count_male_high_bmi_stroke)
percentagemale_high_bmi_stroke = count_male_high_bmi_stroke / len(strokedata_df) * 100

print("Percentage of 'Yes': {:.2f}%".format(percentagemale_high_bmi_stroke['Yes']))
print("Percentage of 'No': {:.2f}%".format(percentagemale_high_bmi_stroke['No']))

male_high_bmi_stroke
Female    2907
No        1241
Yes        833
Name: count, dtype: int64
Percentage of 'Yes': 16.72%
Percentage of 'No': 24.91%


sns.countplot(data=strokedata_df, x='male_high_bmi_stroke', hue='stroke', palette=['blue', 'red'], order=['Yes', 'No'])
plt.xlabel('Males with High BMI & Stroke')
plt.ylabel('Count')
plt.title('Counts of Males with High BMI and Stroke')
plt.legend(title='Stroke', labels=['Yes', 'No'])
plt.show()
#this shows the amount of males with high BMI and stroke

<ipython-input-306-5f524f4f9ebf>:1: UserWarning: The palette list has more values (2) than needed (1), which may not be intended.
  sns.countplot(data=strokedata_df, x='male_high_bmi_stroke', hue='stroke', palette=['blue', 'red'], order=['Yes', 'No'])


strokedata_df.head()


strokedata_df.describe()


import seaborn as sns
import matplotlib.pyplot as plt

# Assuming strokedata_df is your DataFrame containing the data
# Example strokedata_df:
# strokedata_df = pd.read_csv('your_data.csv')

# Selecting numeric columns
numerical_df = strokedata_df.select_dtypes(include=['number'])

# Creating a pairplot
sns.pairplot(numerical_df)
plt.suptitle('Pairwise Correlation Scatter Plot', y=1.02)
plt.show()


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Assuming strokedata_df is your DataFrame containing the data
# Example strokedata_df:
# strokedata_df = pd.read_csv('your_data.csv')

# Dropping non-numeric columns
numerical_df = strokedata_df.select_dtypes(include=['number'])

# Calculate correlations
correlation_matrix = numerical_df.corr()

# Plotting the correlation matrix
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", annot_kws={"size": 10})
plt.title('Correlation Matrix of Strokedata_df Variables')
plt.show()


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Assuming strokedata_df is your DataFrame containing the data
# Example strokedata_df:
# strokedata_df = pd.read_csv('your_data.csv')

# Encoding categorical columns
strokedata_df_encoded = pd.get_dummies(strokedata_df)

# Calculate correlations
correlation_matrix = strokedata_df_encoded.corr()

# Plotting the correlation matrix
plt.figure(figsize=(12, 12))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", annot_kws={"size": 10})
plt.title('Correlation Matrix of Strokedata_df Variables')
plt.show()

#too much going on here so reducing it a little more to focus on specific variables rather than all


# Select the desired columns
selected_columns = ['smoking_status_smokes', 'smoking_status_formerly smoked', 'stroke', 'bmi', 'gender_Female', 'gender_Male', 'hypertension', 'avg_glucose_level', 'heart_disease']

# Extract the sub-DataFrame
selected_data = strokedata_df_encoded[selected_columns]

# Calculate the correlation matrix for the selected columns
correlation_matrix = selected_data.corr()

# Plot the correlation matrix as a heatmap
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()

#Avg glucose level and bmi are correlated (positively), no obvious gender differences


import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report

strokedata_df = pd.read_csv('/content/sample_data/brain_stroke.csv')

# Define target variable
X = strokedata_df.drop(columns=['stroke'])  # Features
y = strokedata_df['stroke']  # Target variable

# Splitting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Preprocessing: One-hot encoding for categorical variables
categorical_cols = [col for col in X.columns if X[col].dtype == 'object']
preprocessor = ColumnTransformer(
    transformers=[('cat', OneHotEncoder(), categorical_cols)],
    remainder='passthrough')

# Define the pipeline with preprocessing and logistic regression
pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', LogisticRegression())
])

# Train the model
pipeline.fit(X_train, y_train)

# Predictions on the testing set
y_pred = pipeline.predict(X_test)

# Evaluating the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# Classification report
print("\nClassification Report:")
print(classification_report(y_test, y_pred))

Accuracy: 0.9458375125376128

Classification Report:
              precision    recall  f1-score   support

           0       0.95      1.00      0.97       943
           1       0.00      0.00      0.00        54

    accuracy                           0.95       997
   macro avg       0.47      0.50      0.49       997
weighted avg       0.89      0.95      0.92       997

/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))


from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt

# Get predicted probabilities for the positive class (class 1)
y_prob = pipeline.predict_proba(X_test)[:, 1]

# Compute ROC curve and AUC
fpr, tpr, thresholds = roc_curve(y_test, y_prob)
roc_auc = auc(fpr, tpr)

# Plot ROC curve
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='blue', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='red', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc='lower right')
plt.show()


from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, classification_report

# Split data into features and target
X = strokedata_df.drop(columns=['stroke'])
y = strokedata_df['stroke']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define preprocessor if needed (example: standardization)
preprocessor = StandardScaler()
# Preprocessing: One-hot encoding for categorical variables
categorical_cols = [col for col in X.columns if X[col].dtype == 'object']
preprocessor = ColumnTransformer(
    transformers=[('cat', OneHotEncoder(), categorical_cols)],
    remainder='passthrough')

# Define the pipeline with preprocessing and kNN classifier
pipeline_knn = Pipeline([
    ('preprocessor', preprocessor),  # Include preprocessor if necessary
    ('classifier', KNeighborsClassifier())  # kNN classifier
])

# Train the kNN model
pipeline_knn.fit(X_train, y_train)

# Predictions on the testing set
y_pred_knn = pipeline_knn.predict(X_test)

# Evaluating the kNN model
accuracy_knn = accuracy_score(y_test, y_pred_knn)
print("kNN Accuracy:", accuracy_knn)

# Classification report for kNN
print("\nkNN Classification Report:")
print(classification_report(y_test, y_pred_knn))

kNN Accuracy: 0.9418254764292878

kNN Classification Report:
              precision    recall  f1-score   support

           0       0.95      0.99      0.97       943
           1       0.17      0.02      0.03        54

    accuracy                           0.94       997
   macro avg       0.56      0.51      0.50       997
weighted avg       0.90      0.94      0.92       997


from sklearn.neighbors import KNeighborsClassifier

# Define the pipeline with preprocessing and kNN classifier
pipeline_knn = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', KNeighborsClassifier())  # kNN classifier
])

# Training the kNN model
pipeline_knn.fit(X_train, y_train)

# Predictions on the testing set
y_pred_knn = pipeline_knn.predict(X_test)

# Evaluating the kNN model
accuracy_knn = accuracy_score(y_test, y_pred_knn)
print("kNN Accuracy:", accuracy_knn)

# Classification report for kNN
print("\nkNN Classification Report:")
print(classification_report(y_test, y_pred_knn))
#0.94 accuracy is good but lets cross-validate

kNN Accuracy: 0.9418254764292878

kNN Classification Report:
              precision    recall  f1-score   support

           0       0.95      0.99      0.97       943
           1       0.17      0.02      0.03        54

    accuracy                           0.94       997
   macro avg       0.56      0.51      0.50       997
weighted avg       0.90      0.94      0.92       997


from sklearn.model_selection import cross_val_score

# Perform 5-fold cross-validation
cv_scores = cross_val_score(pipeline_knn, X, y, cv=5)

# Print cross-validation scores
print("Cross-validation scores:", cv_scores)

# Calculate mean cross-validation score
mean_cv_score = cv_scores.mean()
print("Mean Cross-validation score:", mean_cv_score)
#0.94 cross-validation score is good

Cross-validation scores: [0.94182548 0.93875502 0.937751   0.9497992  0.94176707]
Mean Cross-validation score: 0.9419795531171828


from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns

# Compute confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred_knn)

# Plot confusion matrix
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)
plt.xlabel('Predicted labels')
plt.ylabel('True labels')
plt.title('Confusion Matrix')
plt.show()
#lots of true positives


from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt

# Get predicted probabilities for the positive class (class 1)
y_prob_knn = pipeline_knn.predict_proba(X_test)[:, 1]

# Compute ROC curve and AUC for kNN
fpr_knn, tpr_knn, thresholds_knn = roc_curve(y_test, y_prob_knn)
roc_auc_knn = auc(fpr_knn, tpr_knn)

# Plot ROC curve for kNN
plt.figure(figsize=(8, 6))
plt.plot(fpr_knn, tpr_knn, color='green', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_knn)
plt.plot([0, 1], [0, 1], color='red', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve for kNN')
plt.legend(loc='lower right')
plt.show()

#0.63 AUC is not terrible, but is not great. Could be improved


import seaborn as sns
import matplotlib.pyplot as plt

# Define the column names of the features you want to include in the scatter plots
features_subset = ['age', 'bmi', 'avg_glucose_level']

# Combine features and target variable into a single DataFrame
data_combined = X_train[features_subset].copy()
data_combined['stroke'] = y_train

# Create pair plots for the selected features
sns.pairplot(data=data_combined, vars=features_subset, hue='stroke', palette='husl')
plt.suptitle('Pairwise Scatter Plots with Class Labels', y=1.02)
plt.show()


%%shell
!pip install nbconvert
import os
!jupyter nbconvert --to html '/content/drive/MyDrive/Colab Noteb

/bin/bash: line 1: !pip: command not found
/bin/bash: line 2: import: command not found
/bin/bash: line 3: !jupyter: command not found

---------------------------------------------------------------------------
CalledProcessError                        Traceback (most recent call last)
<ipython-input-321-10be0ab353db> in <cell line: 1>()
----> 1 get_ipython().run_cell_magic('shell', '', "!pip install nbconvert\nimport os\n!jupyter nbconvert --to html '/content/drive/MyDrive/Colab Notebooks/Project Milestone 3.ipynb'\n")

/usr/local/lib/python3.10/dist-packages/google/colab/_shell.py in run_cell_magic(self, magic_name, line, cell)
    332     if line and not cell:
    333       cell = ' '
--> 334     return super().run_cell_magic(magic_name, line, cell)
    335 
    336 

/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
   2471             with self.builtin_trap:
   2472                 args = (magic_arg_s, cell)
-> 2473                 result = fn(*args, **kwargs)
   2474             return result
   2475 

/usr/local/lib/python3.10/dist-packages/google/colab/_system_commands.py in _shell_cell_magic(args, cmd)
    110   result = _run_command(cmd, clear_streamed_output=False)
    111   if not parsed_args.ignore_errors:
--> 112     result.check_returncode()
    113   return result
    114 

/usr/local/lib/python3.10/dist-packages/google/colab/_system_commands.py in check_returncode(self)
    135   def check_returncode(self):
    136     if self.returncode:
--> 137       raise subprocess.CalledProcessError(
    138           returncode=self.returncode, cmd=self.args, output=self.output
    139       )

CalledProcessError: Command '!pip install nbconvert
import os
!jupyter nbconvert --to html '/content/drive/MyDrive/Colab Notebooks/Project Milestone 3.ipynb'
' returned non-zero exit status 127.


from google.colab import drive
drive.mount('/content/drive')

	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
0	Male	67.0	0	1	Yes	Private	Urban	228.69	36.6	formerly smoked	1
1	Male	80.0	0	1	Yes	Private	Rural	105.92	32.5	never smoked	1
2	Female	49.0	0	0	Yes	Private	Urban	171.23	34.4	smokes	1
3	Female	79.0	1	0	Yes	Self-employed	Rural	174.12	24.0	never smoked	1
4	Male	81.0	0	0	Yes	Private	Urban	186.21	29.0	formerly smoked	1

	age	avg_glucose_level	bmi
count	4981.000000	4981.000000	4981.000000
mean	43.419859	105.943562	28.498173
std	22.662755	45.075373	6.790464
min	0.080000	55.120000	14.000000
25%	25.000000	77.230000	23.700000
50%	45.000000	91.850000	28.100000
75%	61.000000	113.860000	32.600000
max	82.000000	271.740000	48.900000

	age	avg_glucose_level	bmi
count	4981.000000	4981.000000	4981.000000
mean	43.419859	105.943562	28.498173
std	22.662755	45.075373	6.790464
min	0.080000	55.120000	14.000000
25%	25.000000	77.230000	23.700000
50%	45.000000	91.850000	28.100000
75%	61.000000	113.860000	32.600000
max	82.000000	271.740000	48.900000

Project Goals¶

Table 1: Stroke Data¶

	gender	age	hypertension	heart_disease	Residence_type	avg_glucose_level	bmi	smoking_status	stroke	hypertension_and_stroke	high_bmi_stroke	female_high_bmi_stroke
0	Male	67.0	True	True	Urban	228.69	36.6	formerly smoked	True	True	Yes	Male
1	Male	80.0	True	True	Rural	105.92	32.5	never smoked	True	True	Yes	Male
2	Female	49.0	True	True	Urban	171.23	34.4	smokes	True	True	Yes	Yes
3	Female	79.0	True	True	Rural	174.12	24.0	never smoked	True	True	No	No
4	Male	81.0	True	True	Urban	186.21	29.0	formerly smoked	True	True	No	Male

	gender	age	hypertension	heart_disease	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
0	Male	67.00	0	1	Urban	228.69	36.6	formerly smoked	1
1	Male	80.00	0	1	Rural	105.92	32.5	never smoked	1
2	Female	49.00	0	0	Urban	171.23	34.4	smokes	1
3	Female	79.00	1	0	Rural	174.12	24.0	never smoked	1
4	Male	81.00	0	0	Urban	186.21	29.0	formerly smoked	1
5	Male	74.00	1	1	Rural	70.09	27.4	never smoked	1
6	Female	69.00	0	0	Urban	94.39	22.8	never smoked	1
7	Female	78.00	0	0	Urban	58.57	24.2	Unknown	1
8	Female	81.00	1	0	Rural	80.43	29.7	never smoked	1
9	Female	61.00	0	1	Rural	120.46	36.8	smokes	1
10	Female	54.00	0	0	Urban	104.51	27.3	smokes	1
11	Female	79.00	0	1	Urban	214.09	28.2	never smoked	1
12	Female	50.00	1	0	Rural	167.41	30.9	never smoked	1
13	Male	64.00	0	1	Urban	191.61	37.5	smokes	1
14	Male	75.00	1	0	Urban	221.29	25.8	smokes	1
15	Female	60.00	0	0	Urban	89.22	37.8	never smoked	1
16	Female	71.00	0	0	Rural	193.94	22.4	smokes	1
17	Female	52.00	1	0	Urban	233.29	48.9	never smoked	1
18	Female	79.00	0	0	Urban	228.70	26.6	never smoked	1
19	Male	82.00	0	1	Rural	208.30	32.5	Unknown	1
20	Male	71.00	0	0	Urban	102.87	27.2	formerly smoked	1
21	Male	80.00	0	0	Rural	104.12	23.5	never smoked	1
22	Female	65.00	0	0	Rural	100.98	28.2	formerly smoked	1
23	Male	69.00	0	1	Urban	195.23	28.3	smokes	1
24	Male	57.00	1	0	Urban	212.08	44.2	smokes	1
25	Male	42.00	0	0	Rural	83.41	25.4	Unknown	1
26	Female	82.00	1	0	Urban	196.92	22.2	never smoked	1
27	Male	80.00	0	1	Urban	252.72	30.5	formerly smoked	1
28	Male	48.00	0	0	Urban	84.20	29.7	never smoked	1
29	Female	82.00	1	1	Rural	84.03	26.5	formerly smoked	1
30	Male	74.00	0	0	Rural	219.72	33.7	formerly smoked	1
31	Female	72.00	1	0	Rural	74.63	23.1	formerly smoked	1
32	Male	58.00	0	0	Rural	92.62	32.0	Unknown	1
33	Female	49.00	0	0	Urban	60.91	29.9	never smoked	1
34	Male	78.00	0	0	Rural	78.03	23.9	formerly smoked	1
35	Male	54.00	0	0	Urban	71.22	28.5	never smoked	1
36	Male	82.00	0	1	Urban	144.90	26.4	smokes	1
37	Male	60.00	1	0	Urban	213.03	20.2	smokes	1
38	Male	76.00	1	0	Rural	243.58	33.6	never smoked	1
39	Female	58.00	0	0	Urban	107.26	38.6	formerly smoked	1